Fulin Cai received his bachelor’s and master’s degrees from Shenzhen University, Shenzhen, China, in 2016 and 2019, respectively. He is currently pursuing a Ph.D. degree in computer engineering at Arizona State University, Tempe, AZ, USA. He is a Graduate Research Assistant with the ASU-Mayo Center for Innovative Imaging, at Arizona State University. He is particularly interested in leveraging advanced deep learning techniques to enhance medical applications and improve healthcare outcomes. His current research interests include deep learning, biomedical signals, radar sensing, and health informatics.

Dr. Teresa Wu is a Professor of Industrial Engineering at the School of Computing and Augmented Intelligence of Arizona State University. She received her B.Sc. and M.Sc. from the Department of Mechanical and Automation Engineering, BeiHang University, and her doctorate from the University of Iowa. She is a National Science Foundation (NSF) CAREER award winner (2003) and ASU tenure and promotion exemplar (2006). Her main areas of interest are in medical imaging analytics, information systems, and distributed decision support. In 2018, she was appointed as a Professor of Radiology (by courtesy) at Mayo Clinic. She is the founding co-director of ASU-Mayo Center for Innovative Imaging.

Dr. Fleming Y. M. Lure received his Ph.D. degree in electrical engineering from Pennsylvania State University, State College, PA, USA, in 1990. He is a Chief Product Officer with MS Technologies Corporation, Rockville, MD, USA. His research interests include computer-aided detection (CAD), machine learning, and deep learning for disease detection, diagnosis, and prognosis on radiological images and portable devices. He has led a team to receive the first FDA pre-market approved (PMA) early-stage lung cancer detection system on radiograph. He has received more than ten SBIR/STTR Phase I/II awards from NIH and DoD to develop and commercialize computer-aided diagnosis (CAD) systems on lung cancer, tuberculosis, and Alzheimer’s disease on radiological images and fall detection from low-cost portable radar. He is also leading a team with interdisciplinary backgrounds to develop a smart imagery framing and truthing (SIFT) system to automatically annotate large amounts of COVID and tuberculosis radiographs to support various governments and consortiums to provide high-quality annotated information for the training of artificial intelligence (AI) and clinical researchers worldwide. He is a member of the Radiological Society of North America (RSNA).